The evolution of gene expression and binding specificity of the largest transcription factor family in primates
Bibliographic record
Abstract
The KRAB-containing zinc finger (KRAB-ZF) proteins represent the largest family of transcription factors (TFs) in humans, yet for the great majority, their function and specific genomic target remain unknown. However, it has been shown that a large fraction of these genes arose from segmental duplications, and that they have expanded in gene and zinc finger number throughout vertebrate evolution. To determine whether this expansion is linked to selective pressures acting on different domains, we have manually curated all KRAB-ZF genes present in the human genome together with their orthologous genes in three closely related species and assessed the evolutionary forces acting at the sequence level as well as on their expression profiles. We provide evidence that KRAB-ZFs can be separated into two categories according to the polymorphism present in their DNA-contacting residues. Those carrying a nonsynonymous single nucleotide polymorphism (SNP) in their DNA-contacting amino acids exhibit significantly reduced expression in all tissues, have emerged in a recent lineage, and seem to be less strongly constrained evolutionarily than those without such a polymorphism. This work provides evidence for a link between age of the TF, as well as polymorphism in their DNA-contacting residues and expression levels-both of which may be jointly affected by selection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".